Interpreting Multiple Regression Output for Business Statistics

Building on our regression model, we add more predictors and interpret the output to see how they improve the model. We begin by checking the assumptions for multiple regression – outliers, normality, homoscedasticity, independence of observations, and multicollinearity – using the output and checks like standardized residuals, Q-Q plot, Durbin-Watson, tolerance, and VIF. Then we add two more predictor to our original model and watch how the parameters change and whether the r-squared change justifies keeping them.
This lecture was recorded on Monday, November 2, 2020 at Missouri State University for QBA 337 – Applied Business Statistics

Music
50 Ways to Clean Your Data – The Spurious Correlations, from the unreleased album of statistical parodies Dark Side of the Mu

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Видео Interpreting Multiple Regression Output for Business Statistics канала Research By Design